A Literature Review on Text Mining With Different
Techniques
V.Sudheer Goud
1, Prof. P. Premchand
21
Research Scholar, Acharya Nagarjuna University, Nagarjuna Nagar, Guntur, A.P, India
2
Professor, Department of Computer Science and Engineering, University College of
Engineering, Osmania University, Hyderabad, T.S, India.
ABSTRACT:
Text mining referred as intelligent text analysis or
text data mining in text and it refers generally to the
method of mining or retrieving, knowledgeable and
non-trivial information and knowledge from
unstructured text. Unstructured text has huge amount
information which is not easily used by the computer
for processing. So that we require certain techniques
to accomplish this task for extracting required
patterns. Text mining plays an important role of
extracting useful patterns from unstructured text. It is
one of the emerging technologies for Knowledge
Discovery Process. Document organization and
pattern discovery becomes the main task in data
mining. In this paper, a review of Text mining and its
techniques, applications, merits and demerits of text
mining have been presented.
KEYWORDS: Text mining, information extraction,
summarization, topic tracking, classification
1. INTRODUCTION
Text Mining is the process of analyzing naturally
occurring text for the purpose of discovering and
capturing semantic information for insertion and
storage in a Knowledge Organization Structure
(KOS) with the ultimate goal of enabling knowledge
discovery via either textual or visual access for use in
a wide range of significant applications. Text Mining
is defined as the computational process of extracting
useful information from massive amounts of digital
data by mapping low-level data into richer, more
abstract forms and by detecting meaningful patterns
implicitly present in the data. TM is the discovery by
computer of new, previously unknown information,
by automatically extracting information from
different written resources. A key element is the
linking together of the extracted information, together
to form new facts or new hypotheses to be explored
further by more conventional means of
experimentation. Text Mining falls into an area called
information extraction. Information extraction (IE)
software identifies and removes relevant information
from texts, pulling information from a variety of
sources and aggregates it, to create a single view. IE
translates content into a homogeneous form through
technologies like extensible Mark-up Language
(XML). The goal of IE software is to transform texts
composed of everyday language into a structured,
database format. In this way, heterogeneous
documents are summarized and presented in a
uniform manner
Text mining has engrossed mounting significance
and has been dynamically applied in knowledge
management. Finding helpful facts or nuggets of
knowledge in databases of text is the spirit of text
mining, an analysis practice that attempts to uncover
hidden patterns in unstructured text data. Text
Mining is presently being used in knowledge
discovery and business intelligence applications
ranging from human resource management to market
intelligence to research and development. Its
techniques are also being used to extend conventional
information retrieval systems with features that create
a more interactive and contextually aware search
experience. Powerful information technology
techniques now exist to identify the relevant S&T
literatures and extract the required information.
Techniques help to:
i. Substantially enhance the retrieval of useful
information from global S&T databases;
ii. Identify the technology infrastructure
(authors, journals, organizations) of a
technical domain;
iii. Identify experts for innovation-enhancing
technical workshops and review panels;
iv. Develop site visitation strategies for
assessment of prolific organizations
globally;
v. Generate technical taxonomies
(classification schemes) with human-based
and computer-based clustering methods; 6.
Estimate global levels of emphasis in
targeted technical areas;
vi. Provide roadmaps for tracking myriad
research impacts across time and
applications areas.
3. RELATED WORK
Yuefeng Li et al [13]: A Text mining and
classification method has been used term-based
approaches. The problems of polysemy and
synonymy are one of the major issues. There was a
hypothesis that pattern-based methods should
outperform best compare to the term-based ones in
describing user preferences. A large scale pattern
remains a hard problem in text mining. The
stateof-the-art term-based methods and the pattern based
methods in proposed model which performs
efficiently. In this work fclustering algorithm is used.
Relevance feature discovery based on both positive
and negative feedback for text mining models.
Jian ma et al [4]: The author focused towards the
problem by classifying text documents on
axiomatically, for the most part in English. When
work with non-English language texts it leads to the
forbiddance. Ontology-based text mining approach
has been used. Its efficient and effective for
clustering research proposals encapsulated with the
English and Chinese texts using a SOM algorithm.
This method can be expanded to help in searching a
better match between proposals and reviewers.
Chien-Liang Liu et al [2]: The paper concluded that
the information about the movie-rating is based on
the result of sentiment-classification. The
feature-based summarizations are used to generate condensed
descriptions of movie reviews. The author designed a
latent semantic analysis (LSA) to establish product
features. It is a way to reduce the size of summary
from LSA. They account both accuracy of sentiment
the system by using a clustering algorithm.
OpenNLP2 tool is used for implementation.
Yue Hu et al [19]: PPSGen is a new system which
was proposed to solicitation of the presentation slides
been generated can be used as drafts. It helps them to
prepare the formal slides in a faster way for the
proprietor. PPSGen system can bring out slides with
better quality suggested by the author. The system
was developed by the Hierarchical agglomeration
algorithm. Tools are a Microsoft Power- Point and
OpenOffice. A 200 combo of papers and slides are
taken as tests set from the web demonstrate for
evaluation process. PPSGen is comparably better
than the baseline methods that were evident by the
user study.
Xiuzhen Zhang et al [10]: The problem faced by all
the reputation system is concentrated by the
author. However the reputation scores are universally
high for sellers. It is a situation requiring great effort
for promising buyers to select trustworthy sellers.
Author proposed CommTrust for trust evaluation by
feedback comments through mining. A
multidimensional trust model is used for computation
job. Data set are collected from ebay, amazon. In this
technique used a Lexical-LDA algorithm.
CommTrust can effectively address the good
reputation problem issue and rank sellers are finally
by showing definitely through the extensive
experiments on eBay and Amazon data.
Dnyanesh G. Rajpathak et al [9]: The challenging
task is In-time augmentation of D-matrix
through the finding of new symptoms and failure
modes. Proposed strategy is to construct the fault
diagnosis ontology abide with concepts and
relationships frequently observed in the fault
diagnosis domain. The needed artifacts and their
dependencies from the unstructured repair verbatim
text were found out by the ontology. Real-life data
collected from the automobile domain. Text mining
algorithms are used. To establish automatically the
D-matrices by the unstructured repair verbatim data
that was mined done by the ontology based text
mining composed while fault diagnosis. A graph and
the graph comparison algorithms have to be
generated for each D-matrix.
Jehoshua Eliashberg et al [11]: To forecast the box
office performance of a movie at the crenulation
point, it’s suitable only if it holds the script and
production cost. They extract textual features in three
levels particularly genre and content, semantics, and
bag-of- words from scripts using domain knowledge
of screenwriting, input given by human, and natural
language processing techniques. A kernel-based
approach is to assess box office performance. Data
set are collected from 300 movie shooting scripts.
The proposed methodology predicts box office
income more exactly 29 percent is reduced mean
squared error (MSE) compared to benchmark
methods.
Donald E. Brown et al [17]: Rail accidents present
image of a valuable safety point for the
transportation industry in many countries. The
Federal Railroad Administration needs the railroads
muddled in accidents to submit reports. The report
has to be cuddled with default field entries and
narratives. A combination of techniques is to
automatically discover accident characteristics that
can inform a better understanding of the patron to the
mining looks at ways to extract features from text
that takes advantage of language characteristics
particular to the rail transport industry.
Luís Filipe da Cruz Nassif et al [6]: In forensic
analysis that was computerized with millions of files
is usually examined. Unstructured text was found in
most of the files performing analyzing process is
highly challenging revealed by computer examiners.
Document clustering algorithms for the analysis of
computers on forensic department seized in police an
investigation which was suggested by the author.
Variety of mixture of parameters that leads to prompt
of 16 different algorithms consider for evaluation.
K-means, K-medoids, Single, Complete and Average
Link, CSPA are the clustering algorithm are used.
Clustering algorithms motivate to induce clusters
formed by either relevant or irrelevant document
which is used to enhance the expert examiner’s job.
4. NECESSITIES OF TEXT MINING
For comprehensive access to the global S&T
literature, and maximum extraction of useful
information from this literature, five primary
conditions are required.
1. A large fraction of the S&T conducted globally
must be documented - information
comprehensiveness.
2. The documentation describing each S&T project
must have sufficient information content to satisfy
the analysis requirements - information quality.
3. A large fraction of these documents must be
retrieved for analysis - information retrieval.
4. Techniques and protocols must be available for
extracting useful information from the retrieved
documents - information extraction.
5. Technical domain and information technology
experts must be closely involved with every step of
the information retrieval and extraction processes -
technical expertise.
5. CONCLUSION
So in this paper, our focus is basically what text
mining is and why it is useful. We have also
discussed requirements of text mining, finally we
taken review of different author’s regards text
mining. In next paper we will discuss about text
mining process and its applications.
6. REFERENCES
[1] Luis Tari, Phan Huy Tu,Jorg Hakenberg, Yi Chen, Tran Cao Son, Graciela Gonzalez, And Chitta Baral,”Incremental Information Extraction Using Relational Databases”, IEEE Transactions On Knowledge And Data Engineering, Vol. 24, No. 1, January 2012.
[2] Chien-Liang Liu, Wen-Hoar Hsaio, Chia-Hoang Lee, Gen-Chi Lu, And Emery Jou,” Movie Rating And Review Summarizationin Mobile Environment”, IEEE Transactions On Systems, Man, And Cybernetics—Part C: Applications And Reviews, Vol. 42, No. 3, May 2012.
[3] Fuzhen Zhuang, Ping Luo, Zhiyong Shen, Qing He, Yuhong Xiong, Zhongzhi Shi, And Hui Xiong, “Mining Distinction And Commonality Across Multiple Domains Using Generative Model For Text Classification” IEEE Transactions On Knowledge And Data Engineering, Vol. 24, No. 11, November 2012.
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[5] Charu C. Aggarwal, Yuchen Zhao, And Philip S. Yu, “On The Use Of Side Information For Mining Text Data”, IEEE Transactions On Knowledge And Data Engineering, Vol. 26, No. 6, June 2014.
[6] Luís Filipe Da Cruz Nassif And Eduardo Raul Hruschka,” Document Clustering For Forensic
Analysis: An Approach For Improving Computer Inspection” IEEE Transactions On Information
Forensics And Security, Vol. 8, No. 1, January 2013.
[7] Bo Chen, Wai Lam, Ivor W. Tsang, And Tak-Lam Wong, “Discovering Low-Rank Shared Concept Space For Adapting Text Mining Models” IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 35, No. 6, June 2013.
[8] Francisco Moraes Oliveira-Neto, Lee D. Han, And Myong Kee Jeong.” An Online Self-Learning Algorithm for License Plate Matching”, IEEE Transactions On Intelligent Transportation Systems, Vol. 14, No. 4, December 2013.
[9] Dnyanesh G. Rajpathak And Satnam Singh,” An Ontology-Based Text Mining Method To Develop D-Matrix From Unstructured Text”, IEEE Transactions On Systems, Man, And Cybernetics: Systems, Vol. 44, No. 7, July 2014.
[10] Xiuzhen Zhang, Lishan Cui, And Yan Wang, “Commtrust: Computing Multi-Dimensional Trust By Mining E-Commerce Feedback Comments”, IEEE Transactions On Knowledge And Data Engineering, Vol. 26, No. 7, July 2014.
[11] Jehoshua Eliashberg, Sam K. Hui, And Z. John Zhang,” Assessing Box Office Performance Using Movie
Scripts: A Kernel-Based Approach”, IEEE Transactions On
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[14] Kamal Taha,” Extracting Various Classes Of Data From Biological Text Using The Concept Of Existence Dependency”, IEEE Journal Of Biomedical And Health Informatics, Vol. 19, No. 6, November 2015.
[15] Shuhui Jiang, Xueming Qian, Jialie Shen, Yun Fu And Tao Mei, “Author Topic Model-Based Collaborative Filtering For Personalized Poi Recommendations”, IEEE Transactions On Multimedia, Vol. 17, No. 6, June 2015.
[16] Beichen Wang, Xiaodong Chen, Hiroshi Mamitsuka, And Shanfeng Zhu,” Bmexpert: Mining Medline For
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6, November/December 2015.
[17] Donald E. Brown,” Text Mining The Contributors To Rail Accidents”, IEEE Transactions On Intelligent Transportation Systems, Vol. 17, No. 2, February 2016.
BIBLOGRAPHY
V.Sudheer Goud, Post Graduated
in Master of Computer Application (MCA) from OU,1994 , Post Graduated in Master of Business
Administration (MBA) from
Engineering (M.Tech) from IETE , Hyderabad in 2013 and Pursuing Phd in Computer Science in ANU. He is currently working as an Associate Professor, Department of Computer Science in Holy
Mary Institute of Technology and Science (HITS),
(V) Bogaram, (M) Keesara, Medchal .Dist,
Telangana, India. He has 23 years of Teaching Experience. His research interests include, Data Mining, Cloud Computing and Information Security.
Prof. P. Premchand, He is
currently working as an Professor, Department of Computer Science
and Engineering, University
College of Engineering, Osmania University, Hyderabad, Telangana State, India.
